Subtle Structural Changes Research Articles

Future Changes in the Vertical Structure of Severe Convective Storm Environments over the U.S. Central Great Plains

Abstract The effect of warming on severe convective storm potential is commonly explained in terms of changes in vertically integrated (“bulk”) environmental parameters, such as CAPE and 0–6-km shear. However, such events are known to depend on the details of the vertical structure of the thermodynamic and kinematic environment that can change independently of these bulk parameters. This work examines how warming may affect the complete vertical structure of these environments for fixed ranges of values of high CAPE and bulk shear, using data over the central Great Plains from two high-performing climate models (CNRM and MPI). To first order, projected changes in the vertical sounding structure are consistent between the two models: the environment warms approximately uniformly with height at constant relative humidity, and the shear profile remains relatively constant. The boundary layer becomes slightly drier (−2% to 6% relative humidity) while the free troposphere becomes slightly moister (+1% to 3%), with a slight increase in moist static energy deficit aloft with stronger magnitude in CNRM. CNRM indicates enhanced low-level shear and storm-relative helicity associated with stronger hodograph curvature in the lowest 2 km, whereas MPI shows near-zero change. Both models strongly underestimate shear below 1 km compared to ERA5, indicating large uncertainty in projecting subtle changes in the low-level flow structure in climate models. The evaluation of the net effect of these modest thermodynamic and kinematic changes on severe convective storm outcomes cannot be ascertained here but could be explored in simulation experiments. Significance Statement Severe thunderstorms and tornadoes cause substantial damage and loss of life each year, which raise concerns about how they may change as the world warms. We typically use a small number of common atmospheric parameters to understand how these localized events may change with climate change. However, climate change may alter the weather patterns that produce these events in ways not captured by these parameters. This work examines how climate change may alter the complete vertical structure of temperature, moisture, and wind and discusses the potential implications of these changes for future severe thunderstorms and tornadoes.

Deep learning assists early-detection of hypertension-mediated heart change on ECG signals.

Arterial hypertension is a major risk factor for cardiovascular diseases. While cardiac ultrasound is a typical way to diagnose hypertension-mediated heart change, it often fails to detect early subtle structural changes. Electrocardiogram(ECG) represents electrical activity of heart muscle, affected by the changes in heart's structure. It is crucial to explore whether ECG can capture slight signals of hypertension-mediated heart change. However, reading ECG records is complex and some signals are too subtle to be captured by cardiologist's visual inspection. In this study, we designed a deep learning model to predict hypertension on ECG signals and then to identify hypertension-associated ECG segments. From The First Affiliated Hospital of Xiamen University, we collected 210,120 10-s 12-lead ECGs using the FX-8322 manufactured by FUKUDA and 812 ECGs using the RAGE-12 manufactured by NALONG. We proposed a deep learning framework, including MML-Net, a multi-branch, multi-scale LSTM neural network to evaluate the potential of ECG signals to detect hypertension, and ECG-XAI, an ECG-oriented wave-alignment AI explanation pipeline to identify hypertension-associated ECG segments. MML-Net achieved an 82% recall and an 87% precision in the testing, and an 80% recall and an 82% precision in the independent testing. In contrast, experienced clinical cardiologists typically attain recall rates ranging from 30 to 50% by visual inspection. The experiments demonstrate that ECG signals are sensitive to slight changes in heart structure caused by hypertension. ECG-XAI detects that R-wave and P-wave are the hypertension-associated ECG segments. The proposed framework has the potential to facilitate early diagnosis of heart change.

Metal-Ligand Covalency in the Valence Excited States of Metal Dithiolenes Revealed by S 1s3p Resonant Inelastic X-ray Scattering.

Metallo dithiolene complexes with biological and catalytic relevance are well-known for having strong metal-ligand covalency, which dictates their valence electronic structures. We present the resonant sulfur Kβ (1s3p) X-ray emission spectroscopy (XES) for a series of Ni and Cu bis(dithiolene) complexes to reveal the ligand sulfur contributions to both the occupied and unoccupied valence orbitals. While S K-edge X-ray absorption spectroscopy played a critical role in identifying the covalency of the unoccupied orbitals of metal dithiolenes, the present focus on XES explores the occupied density of states. For a series of [Cu(mnt)2]n- and [Ni(mnt)2]n- anions and dianions, a comparison of the nonresonant and resonant S Kβ XES spectra highlights the dramatic improvement in spectral resolution and corresponding ability to differentiate subtle changes in occupied electronic structure across the series. Furthermore, the use of resonant inelastic X-ray scattering (RIXS) probes the valence excited states and the core-valence couplings of the complexes. By employing a theoretical approach based on time-dependent density functional theory to interpret the RIXS spectra, we reveal how metal-ligand covalency influences the excited state energies and covalencies. We identify the low energy excited states as having the same symmetry as the nominal "ligand field" or "d-d" states that typically dominate the photophysics of 3d metal complexes but with significant metal-ligand charge transfer character dictated by their covalency. These results suggest that strong metal-ligand covalency can be used to influence the charge-transfer photochemistry of first row transition metal complexes.

Repairing Noise-Contaminated Low-Frequency Vibrational Spectra with an Attention U-Net.

Low-frequency vibrational modes in infrared (IR) and Raman spectra, often termed molecular fingerprints, are sensitive probes of subtle structural changes and chemical interactions. However, their inherent weakness and susceptibility to environmental interference make them challenging to detect and analyze. To tackle this issue, we developed a deep learning denoising protocol based on an attention-enhanced U-net architecture. This model leverages the inherent correlations between high- and low-frequency vibrational modes within a molecule, effectively reconstructing low-frequency spectral features from their high-frequency counterparts. We demonstrate the effectiveness of this method by recovering low-frequency signals of trans-1,2-bis(4-pyridyl)ethylene (BPE) adsorbed on an Ag surface, a representative system for surface enhancement Raman spectroscopy (SERS). Notably, the trained model exhibits promising transferability to SERS spectra acquired under different surface and external field conditions. Furthermore, we applied this method to experimental IR and Raman spectra of BPE, achieving high-quality, low-frequency spectral recovery.

C-4 analogues of podophyllotoxin as tubulin inhibitors: synthesis, biological evaluation, and structure-activity relationship

The diversity of lignan small molecules derived from podophyllotoxin, a non-covalent tubulin inhibitor isolated from the Podophyllum family, has led to the clinical development of FDA-approved anticancer agents etoposide and teniposide. While these two compounds share the same tetracyclic core as podophyllotoxin, two subtle structural changes—4’ demethylation on the aromatic ring and stereospecific glycosylation at the C-4 hydroxyl—result in an alternate biological mechanism. Given the immense pharmacological importance of altering the C-4 position, we synthesised and evaluated a systematic library of diversified esters to establish a structure-activity relationship regarding modification at C-4 on the properties of podophyllotoxin. We determined the biological activity of these esters through cell viability assays, computer docking models, tubulin polymerisation assays, and cell cycle analysis. Altogether, we demonstrate that increasing steric hindrance at C-4 leads to a loss in potency against human cancer cells but has a significantly lesser impact on cell-free tubulin inhibition.

Rational Molecular Design of Phenanthroimidazole-Based Fluorescent Materials toward High-Efficiency Deep-Blue OLEDs by Molecular Isomer Engineering.

Organic light-emitting diodes (OLEDs) have been extensively investigated in full-color displays and energy-saving lighting owing to their unique advantages. However, deep-blue OLEDs based on nondoped emitting layers with a satisfactory external quantum efficiency (EQE) are still rare for applications. In this work, six hot exciton materials, PPIM-12F, PPIM-22F, PPIM-13F, PPIM-23F, PPIM-1CN, and PPIM-2CN, are designed and synthesized via an isomer engineering design strategy and their photophysical properties and OLED performance are systematically investigated. These emitters all possess wide band gaps (3.53-3.69 eV), hybrid local and charge transfer (HLCT) characteristics, and good thermal stabilities. The C2 series compounds, PPIM-22F, PPIM-23F, and PPIM-2CN, all show redder emission peaks than the N1 series counterparts of PPIM-12F, PPIM-13F, and PPIM-1CN. In addition, the LUMO energy levels decrease consecutively in the sequence of PPIM-22F < PPIM-23F < PPIM-2CN and are all lower than their respective N1 series position isomers of PPIM-12F, PPIM-13F, and PPIM-1CN. The CV measurements indicate that such a design strategy renders the fine-tuning of LUMO energy levels, and the incorporation of electron acceptors at the extended C2 position of the PI unit is a better choice to improve the electron injection ability. Theoretical simulations indicate that they may harvest the triplet exciton through an upper-level reverse intersystem crossing process, which decreases the gathering of triplet excitons and allows the OLEDs to be fabricated by nondoping technology. Among them, PPIM-22F with a difluorobenzene substituent at the C2 position manifests the best performance in OLEDs, which exhibits the maximum EQE of 7.87% and Commission Internationale de ĺEclairage (CIE) coordinates of (0.16, 0.10). This work demonstrates an effective strategy for considerable improvement in device performance by a subtle change in the molecular structure through isomer engineering.

Acute Knee Injury Detection with Magnetic Resonance Imaging (MRI)

The anterior cruciate ligament (ACL) is a major ligament in the knee that helps to stabilize the joint and prevent excessive forward movement of the shinbone. An ACL tear is a common injury, especially among athletes who participate in sports that involve pivoting and sudden changes in direction. This paper proposes an ensemble model, which includes three deep learning models (EfficientNet-B7, ResNet-152V2, and DenseNet-201) and a genetic algorithm, to detect and classify ACL tears using knee magnetic resonance imaging (MRI). The ensemble model was trained on the KneeMRI dataset, which comprises labeled MRI images. The deep learning models can learn to identify subtle changes in ligament structure and signal intensity that are associated with ACL tears and the genetic algorithm is used to find the optimal prediction. The proposed ensemble model was evaluated using the KneeMRI dataset. The dataset was preprocessed using data augmentation techniques. Then, the ensemble model was applied to the KneeMRI dataset, evaluated, and compared with previous models. The accuracy, recall, precision, specificity, and F1 score of our proposed ensemble model were 99.68%, 98.73%, 99.52%, 99.62%, and 98.94%, respectively. Thus, our ensemble model has an unrivaled perceptive outcome and could be used to accurately identify and classify ACL tears, improving patient outcome.

Phase selectivity upon flash-lamp annealing of sputter deposited amorphous titanium oxide films

We report the impact of flash-lamp-annealing (FLA) on the structural evolution of amorphous titania (TiO2) films produced by DC reactive magnetron sputtering. TiO2 films were grown at room-temperature at different oxygen partial pressure (PO2) and subsequently annealed as a function of the FLA energy density. X-ray diffraction confirms that FLA induces phase formation from the initial amorphous state with a general transition from anatase to rutile by increasing the FLA energy density (temperature). Interestingly, the transformation onset of anatase to rutile is achieved at lower energy densities for higher PO2. On the contrary, films with a highly resilient anatase phase can be produced at relatively low PO2. A detailed analysis of the pristine amorphous structure carried out by X-ray absorption near-edge structure indicates the role of oxygen sites in the observed phase transformation. In particular, oxygen vacancies seem to stabilize the anatase phase at high temperatures. The results show the relevance of subtle changes in the initial amorphous structure for phase selectivity in TiO2 films upon FLA.

Effectiveness of Three Turbulence Modeling Approaches in a Crosswind–Sedan–Dune Computational Fluid Dynamics Framework

The aerodynamic loads of a sedan experience significant fluctuations when passing by a sand dune at the roadside under crosswinds, which can easily cause yawing and overturning. Computational fluid dynamics (CFD) methods, based on different turbulence modeling approaches, yield different aerodynamic results for sedans. This study aims to investigate the effects of three prevailing turbulence modeling approaches (renormalization group (RNG) k-ε, large eddy simulation (LES), and improved delayed detached eddy simulation (IDDES)) on the aerodynamic characteristics of a sedan passing by a sand dune under crosswinds. The CFD dynamic mesh models are constructed using the “mosaic” mesh technique to account for the dune–air–sedan interaction. The reliability of the CFD prediction method is verified by comparing it with field test results. The predictive capabilities of the three turbulence modeling approaches are compared in terms of aerodynamic loads and flow field characteristics. The simulation of sand particle movement is conducted through the discrete phase model, aiming to assess the impact of wind–sand flow on the aerodynamic properties of sedans. Corresponding results show that the aerodynamic loads predicted by the LES model closely match (within 4.4–7.5%) the corresponding data obtained from field tests. While the IDDES and LES models demonstrate similar abilities in characterizing the wind field details, and their results exhibit maximum differences of 8.3–15.7%. Meanwhile, the maximum difference between the results obtained by the RNG k-ε and LES models ranges from 14.8% to 18.4%, attributed to its inability to capture subtle changes in the vortex structure within the flow field. This work will provide a numerical modeling reference for studies on the wind–sand flow and the aerodynamic characteristics of sedans running through the desert, and it has implications for the safe driving of sedans under extreme conditions.

Investigating Lipase/Stain Interactions: Determining Interfacial Protein Conformation with Surface Spectroscopy.

Conventional bulk protein structure determination methods are not suitable for understanding the distinct and diverse interactions of proteins with interfaces. Notably, interfacial activation is a feature common to many lipases involving movement of a helical "lid" region upon contact with a hydrophobic surface to expose the catalytic site. Here we use the surface specificity of vibrational sum frequency generation spectroscopy (VSFG) spectroscopy to directly probe the conformation of Thermomyces lanuginosus lipase (TLL) at hydrophobic interfaces. The TLL-catalyzed reaction at the air/water interface is monitored by VSFG spectroscopy, showing loss of ester carbonyl modes and appearance of carboxylate stretching modes of the fatty acid products. Furthermore, comparison of experimental and calculated VSFG spectra of the amide I band of TLL allows us to discern the subtle structural changes involved with lid-opening at a hydrophobic surface. Finally, we report a likely orientation of this lid-open state, which interacts with the surface through a loop region away from the lid and active site. This experimental framework for probing protein structure and function at interfaces addresses a significant problem in protein science that is not only impeding the design of better enzymes for biotechnology applications but also drug discovery targeting membrane associated proteins.

In situ study on articular cartilage degeneration in simulated microgravity by HOF-ATR-FTIR spectroscopy

Fourier transform infrared spectroscopy (FTIRS) can provide rich information on the composition and content of samples, enabling the detection of subtle changes in tissue composition and structure. This study represents the first application of FTIRS to investigate cartilage under microgravity. Simulated microgravity cartilage model was firstly established by tail-suspension (TS) for 7, 14 and 21 days, which would be compared to control samples. A self-developed hollow optical fiber attenuated total reflection (HOF-ATR) probe coupled with a FTIR spectrometer was used for the spectral acquisition of cartilage samples in situ, and one-way analysis of variance (ANOVA) was employed to analyze the changes in the contents of cartilage matrix at different stages. The results indicate that cartilage degenerates in microgravity, the collagen content gradually decreases with the TS time, and the structure of collagen fibers changes. The trends of proteoglycan content and collagen integrity show an initial decrease followed by an increase, ultimately significantly decreasing. The findings provide the basis for the cartilage degeneration in microgravity with TS time, which must be of real significance for space science and health detection.

Preoperative early-stage lung cancer patients and local brain area changes: a cross-sectional observational descriptive study.

Lung cancer is a major global health concern. Patients undergo a substantial process of emotional transformation following a lung cancer diagnosis, during which subtle changes in brain function and/or structure may occur. As such, the present study aimed to investigate the neuroplastic changes induced by negative emotions in patients with early-stage lung cancer. This cross-sectional study recruited 35 patients with early-stage lung cancer and 33 age- and sex-matched healthy control patients. All participants completed the Hamilton Anxiety Rating Scale (HAMA), Hamilton Depression Rating Scale (HAMD), and functional magnetic resonance imaging (fMRI). Amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) were used as the fMRI indices. Correlations between the clinical assessments and ALFF and ReHo values were calculated. Our analysis revealed no significant differences in HAMD and HAMA scores between patients and control patients (p > 0.05). However, significant alterations in ALFF and ReHo were observed in multiple brain regions in patients with early-stage lung cancer compared to healthy controls (P FalseDiscoveryRate < 0.05). Specifically, ALFF values were decreased in the right postcentral gyrus, calcarine, and left middle cingulate, while ReHo values increased in the right angular gyrus and decreased in the bilateral postcentral gyrus, insula, left calcarine, putamen, superior temporal gyrus, middle cingulate, and right Rolandic gyrus. The HAMD score was significantly correlated with the ALFF value in the right postcentral gyrus (P = 0.007). This study provides valuable insights into the adaptive responses of the brain following the early diagnosis of lung cancer, revealing potential disturbances in negative emotional processing. Harnessing neuroplasticity may open new avenues for the establishment of personalized treatment strategies and targeted interventions to support the emotional and mental health of patients with lung cancer.

A low-barrier proton shared between two aspartates acts as a conformational switch that changes the substrate specificity of the β-lactamase BlaC

Serine β-lactamases inactivate β-lactam antibiotics in a two-step mechanism comprising acylation and deacylation. For the deacylation step, a water molecule is activated by a conserved glutamate residue to release the adduct from the enzyme. The third-generation cephalosporin ceftazidime is a poor substrate for the class A β-lactamase BlaC from Mycobacterium tuberculosis but it can be hydrolyzed faster when the active site pocket is enlarged, as was reported for mutant BlaC P167S. The conformational change in the Ω-loop of the P167S mutant displaces the conserved glutamate (Glu166), suggesting it is not required for deacylation of the ceftazidime adduct. Here, we report the characterization of wild type BlaC and BlaC E166A at various pH values. The presence of Glu166 strongly enhances activity against nitrocefin but not ceftazidime, indicating it is indeed not required for deacylation of the adduct of the latter substrate. At high pH wild type BlaC was found to exist in two states, one of which converts ceftazidime much faster, resembling the open state previously reported for the BlaC mutant P167S. The pH-dependent switch between the closed and open states is caused by the loss at high pH of a low-barrier hydrogen bond, a proton shared between Asp172 and Asp179. These results illustrate how readily shifts in substrate specificity can occur as a consequence of subtle changes in protein structure.

Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?

Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions. Through better data integration, feature identification, knowledge representation, validation, etc., neuroinformaticists, are ideally suited to bring clarity, context, and explainabilty to the study of sports-related head injuries in males and in females, and helping to define recovery.

Structural and electronic properties of Ag-, Ni-, and Zn-doped copper chalcogenides – computational studies

Copper chalcogenides are among the most extensively studied materials for their application as thermoelectric materials and narrow-bandgap semiconductors for electrocatalytic purposes. The state-of-the-art literature predominantly adopts an experimental approach, often overlooking issues of stability and subtle changes in electronic structure arising from defect formation or doping in these systems. This study focuses on a computational approach to copper(I) chalcogenides doped with the most common impurities, namely Ag, Ni, and Zn. By considering formation energies and defect formation energies, the study offers a discussion with literature data concerning long-term stability of these systems, particularly under chemical/temperature gradient conditions and related ion migration processes. In subsequent sections, the impact of the presence of chosen impurities, as well as vacancies in the cationic sublattice, on structural parameters and electronic structure is presented. This includes selected electronic structure descriptors, providing insights into the influence of dopants and vacancies on the potential thermoelectric and electrocatalytic properties of copper chalcogenides.

Electrophile Determines Cellular Phenotypes among XPO1-Targeting Small Molecules.

Covalent drug discovery has experienced a renaissance, with numerous electrophilic small molecules recently gaining FDA approval. Many structurally diverse electrophilic small molecules target exportin-1 (XPO1/CRM1) at cysteine 528, including the selective inhibitor of nuclear export (SINE) selinexor, which was FDA-approved as an anticancer agent in 2019. Emerging evidence supports additional pharmacological classes of XPO1 modulators targeting Cys528, including the selective inhibitors of transcriptional activation (SITAs) and probes that induce rapid degradation of XPO1. Here, we analyzed structure-activity relationships across multiple structural series of XPO1 Cys528-targeting probes. We observe that the electrophilic moiety of Cys528-targeting small molecules plays a decisive role in the cellular behavior observed, with subtle changes in electrophile structure being sufficient to convert XPO1-targeting probes to different pharmacological classes. This investigation represents a unique case study in which the electrophile functionality used to target a specific cysteine determines the pharmacological effect among diverse XPO1-targeting small molecules.

Atypical Bone Involvement in Erdheim-Chester Disease Showing No or Subtle Structure Changes and Increased Activity on FDG PET/CT.

Bones are the most common sites involved in Erdheim-Chester disease. On imaging, the bone involvement in Erdheim-Chester disease typically presents as osteosclerotic lesions, and osteolytic lesions are unusual. Bone lesions showing no or subtle structure changes are rarely reported. We describe FDG PET/CT findings in a case of Erdheim-Chester disease with atypical bone involvement. The bone lesions showed no or subtle structure changes without obvious osteosclerosis or osteolysis on radiographs and CT, and increased FDG activity mimicking lymphoma. Familiarity with these atypical imaging findings of bone involvement in Erdheim-Chester disease is helpful for the diagnosis.

Diagnosis of dental caries based on attenuation coefficients analysis of optical coherence tomography images.

Quantitative analysis of optical attenuation based on optical coherence tomographyimages will offer an effective method to enhance diagnostic capabilities. In this paper, the optical attenuation in demineralized caries specimens was calculated to distinguish between normal teeth and carious teeth and further to differentiate the severity of caries, and thus come to the half-automated diagnosis of dental caries. Results show that the attenuation coefficient in carious regions is approximately , while that of normal teeth is about . Attenuation coefficient of carious regions is 35% higher than that of normal teeth. Moreover, five classes of caries were qualified and classified based on the optical attenuation coefficient. Compared with the healthy teeth, there is a noticeable disparity in the attenuation coefficients of carious teeth, both on the surface and at the dentinoenamel junction. This study provides a method for accurate caries diagnosis, particularly in detection of early lesions and subtle structural changes.

Chromogenic recognition of Cr(III) and Cu(II) using a novel Schiff base and “turn-on” fluorescent detection of CO32− by its reduced scaffold in aqueous media

An upsurge of severe toxic inorganic ionic species in water arising due to anthropogenic activities, has caused global concern and detection of such harmful contaminants is a matter of supreme importance. In this regard, a novel trans-1,2-diaminocyclohexane containing Schiff base N,N′-di(4-acetamidophenylmethylene)-1,2-cyclohexanediamine (1) and its reduced scaffold N,N’-di(4-acetamidophenylmethyl)-1,2-cyclohexanediamine (2) have been synthesized and characterized by FT-IR and 1H & 13C NMR while the structures of both 1 and 2 have been determined by X-ray single crystal diffraction analysis. The detection response of 1 and 2 have been investigated toward same set of analytes and found to be entirely different both in absorption and emission studies. Compound 1 selectively and very sensitively detects Cr3+ and Cu2+via hydrolytic rupture of >C = N- bond as indicated by ratiometric change and emergence of an isosbestic point in UV–vis analysis. Further, the kinetic studies clearly suggested rapid hydrolysis of 1 (< 3.0 mins) in the presence of Cr3+ whereas it requires ∼1.0 h for complete hydrolysis with Cu2+. In contrast, the recognition of Cr3+ and Cu2+ not be observed through fluorescence technique. Moreover, 2 remains silent toward Cr3+/Cu2+in UV–vis analysis, however, it selectively recognizes CO32− through ‘turn-on’ fluorescence output in the background presence of several tested cations and anions. Job's plot and mass analysis suggested 1:2 (CO32−: 2) binding stoichiometry between CO32− and 2. Remarkably, the present findings demonstrate entirely different functionality of a Schiff base and its reduced form in presence of same analytes. The main objective of this report is to illustrate that how a subtle structural change of a molecule governs the analyte detection ability.

Radiomics model based on contrast-enhanced computed tomography imaging for early recurrence monitoring after radical resection of AFP-negative hepatocellular carcinoma

BackgroundAlthough radical surgical resection is the most effective treatment for hepatocellular carcinoma (HCC), the high rate of postoperative recurrence remains a major challenge, especially in patients with alpha-fetoprotein (AFP)-negative HCC who lack effective biomarkers for postoperative recurrence surveillance. Emerging radiomics can reveal subtle structural changes in tumors by analyzing preoperative contrast-enhanced computer tomography (CECT) imaging data and may provide new ways to predict early recurrence (recurrence within 2 years) in AFP-negative HCC. In this study, we propose to develop a radiomics model based on preoperative CECT to predict the risk of early recurrence after surgery in AFP-negative HCC.Patients and methodsPatients with AFP-negative HCC who underwent radical resection were included in this study. A computerized tool was used to extract radiomic features from the tumor region of interest (ROI), select the best radiographic features associated with patient’s postoperative recurrence, and use them to construct the radiomics score (RadScore), which was then combined with clinical and follow-up information to comprehensively evaluate the reliability of the model.ResultsA total of 148 patients with AFP-negative HCC were enrolled in this study, and 1,977 radiographic features were extracted from CECT, 2 of which were the features most associated with recurrence in AFP-negative HCC. They had good predictive ability in both the training and validation cohorts, with an area under the ROC curve (AUC) of 0.709 and 0.764, respectively. Tumor number, microvascular invasion (MVI), AGPR and radiomic features were independent risk factors for early postoperative recurrence in patients with AFP-negative HCC. The AUCs of the integrated model in the training and validation cohorts were 0.793 and 0.791, respectively. The integrated model possessed the clinical value of predicting early postoperative recurrence in patients with AFP-negative HCC according to decision curve analysis, which allowed the classification of patients into subgroups of high-risk and low-risk for early recurrence.ConclusionThe nomogram constructed by combining clinical and imaging features has favorable performance in predicting the probability of early postoperative recurrence in AFP-negative HCC patients, which can help optimize the therapeutic decision-making and prognostic assessment of AFP-negative HCC patients.